Why Choose This Course
Employers in the banking and investment sector require graduates with a deep understanding of the relevant mathematical concepts as well as the practical and computational skills associated with applying them. This course provides both, and is an opportunity for students pursuing mathematical degrees that are not specifically financial in nature to continue to studies in advanced mathematics with a financial focus, and therefore enhance their employment options.
The course combines mathematical rigour with an emphasis on computational finance and the opportunity to study machine learning, a combination that is currently unique in Ireland. The opportunity to work in teams is built into the programme structure.
UCC itself enjoys proximity to financial employers in Ireland (for the example in the IFSC in Dublin) and in other European financial centres, including London. Students applying from overseas may also wish to consider Ireland's Third Level Graduate Scheme, details of which are available at http://inis.gov.ie/en/INIS/Pages/Student%20Pathway.
Course Outline
Modern finance is increasingly reliant upon advanced mathematical and computational techniques for the modelling of asset and financial market movements, the design and valuation of financial derivatives, and portfolio management.
This course provides an appropriately rigorous treatment of branches of mathematics applicable to financial modelling, including measure-theoretic probability, stochastic processes in discrete and continuous time, and partial differential equations. It is mathematically challenging and requires prior familiarity with multivariate calculus, differential equations, linear algebra, probability, and statistics. You should also have some experience of programming.
The rapid increase in available computing speeds over the past fifteen years has led to the widespread adoption of sophisticated computational methods for financial modelling and the development of algorithmic approaches to market trading.
Computational methods form a core part of this course; we provide exposure to relevant software including Python, R and C#, and provide the option to study machine learning, which is emerging as an essential and rapidly developing tool in industry.
Comment
In Semesters 1 and 2 you can expect to attend an average of 12 hours of lectures and 6-8 hours of tutorials and lab sessions per week, which will be spread evenly throughout the working day. The remainder of your time will be spent in independent study, exercises and assignments.
Semester 3 will be entirely devoted to a substantial research project which requires you to write, submit and defend a dissertation. Computer labs are provided on campus with all relevant software packages, though students are also encouraged to have access to a laptop of their own.
Teaching at UCC is research-led, and the course is delivered by mathematicians, statisticians and computer scientists who are internationally recognised for their research, ensuring that you will have access to up-to-date knowledge in the field. Relevance to current industry practice is ensured by our Industry Advisory Committee.